Learning – Lecture 4 – CS50's Introduction to Artificial Intelligence with Python 2020

CS50

00:00:00 – Introduction
00:00:15 – Machine Learning
00:01:15 – Supervised Learning
00:08:11 – Nearest-Neighbor Classification
00:12:30 – Perceptron Learning
00:33:19 – Support Vector Machines
00:39:31 – Regression
00:42:37 – Loss Functions
00:49:33 – Overfitting
00:55:44 – Regularization
00:59:42 – scikit-learn
01:09:57 – Reinforcement Learning
01:13:02 – Markov Decision Processes
01:19:56 – Q-learning
01:38:54 – Unsupervised Learning
01:40:19 – k-means Clustering

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

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LICENSE

CC BY-NC-SA 4.0
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
https://creativecommons.org/licenses/by-nc-sa/4.0/

David J. Malan
https://cs.harvard.edu/malan
malan@harvard.edu

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